论文标题
预测是不理解的：识别和解决机器学习中的指定
Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning
论文作者
论文摘要
机器学习（ML）模型通常是针对给定数据集的准确性进行了优化的。但是，此预测标准很少捕获模型的所有理想属性，特别是它与域专家对任务的理解的匹配程度。指定的指定是指多种模型的存在，这些模型在其内域准确性上是无法区分的，即使它们在其他期望的属性（例如分布（OOD）性能）上有所不同。确定这些情况对于评估ML模型的可靠性至关重要。 我们正式化了规定的概念，并提出了一种识别和部分解决它的方法。我们训练多个模型具有独立约束，迫使他们实施不同的功能。他们发现了预测性特征，否则标准经验风险最小化（ERM）却忽略了这些特征，然后我们将其提炼成具有出色OOD性能的全球模型。重要的是，我们限制了模型以与数据歧管保持一致，以确保它们发现有意义的功能。我们在计算机视觉（拼贴，Wilds-camelyon17，GQA）中的多个数据集上演示了该方法，并讨论了规定的一般含义。最值得注意的是，没有其他假设，内域性能无法用于OOD模型选择。
Machine learning (ML) models are typically optimized for their accuracy on a given dataset. However, this predictive criterion rarely captures all desirable properties of a model, in particular how well it matches a domain expert's understanding of a task. Underspecification refers to the existence of multiple models that are indistinguishable in their in-domain accuracy, even though they differ in other desirable properties such as out-of-distribution (OOD) performance. Identifying these situations is critical for assessing the reliability of ML models. We formalize the concept of underspecification and propose a method to identify and partially address it. We train multiple models with an independence constraint that forces them to implement different functions. They discover predictive features that are otherwise ignored by standard empirical risk minimization (ERM), which we then distill into a global model with superior OOD performance. Importantly, we constrain the models to align with the data manifold to ensure that they discover meaningful features. We demonstrate the method on multiple datasets in computer vision (collages, WILDS-Camelyon17, GQA) and discuss general implications of underspecification. Most notably, in-domain performance cannot serve for OOD model selection without additional assumptions.
